Searching PubMed during a Pandemic

Background

The 2009 influenza A(H1N1) pandemic has generated thousands of articles and news items. However, finding relevant scientific articles in such rapidly developing health crises is a major challenge which, in turn, can affect decision-makers'' ability to utilise up-to-date findings and ultimately shape public health interventions. This study set out to show the impact that the inconsistent naming of the pandemic can have on retrieving relevant scientific articles in PubMed/MEDLINE.

Methodology

We first formulated a PubMed search algorithm covering different names of the influenza pandemic and simulated the results that it would have retrieved from weekly searches for relevant new records during the first 10 weeks of the pandemic. To assess the impact of failing to include every term in this search, we then conducted the same searches but omitted in turn “h1n1,” “swine,” “influenza” and “flu” from the search string, and compared the results to those for the full string.

Principal Findings

On average, our core search string identified 44.3 potentially relevant new records at the end of each week. Of these, we determined that an average of 27.8 records were relevant. When we excluded one term from the string, the percentage of records missed out of the total number of relevant records averaged 18.7% for omitting “h1n1,” 13.6% for “swine,” 17.5% for “influenza,” and 20.6% for “flu.”

Conclusions

Due to inconsistent naming, while searching for scientific material about rapidly evolving situations such as the influenza A(H1N1) pandemic, there is a risk that one will miss relevant articles. To address this problem, the international scientific community should agree on nomenclature and the specific name to be used earlier, and the National Library of Medicine in the US could index potentially relevant materials faster and allow publishers to add alert tags to such materials.     

Attentional Control via Parallel Target-Templates in Dual-Target Search

Simultaneous search for two targets has been shown to be slower and less accurate than independent searches for the same two targets. Recent research suggests this ‘dual-target cost’ may be attributable to a limit in the number of target-templates than can guide search at any one time. The current study investigated this possibility by comparing behavioural responses during single- and dual-target searches for targets defined by their orientation. The results revealed an increase in reaction times for dual- compared to single-target searches that was largely independent of the number of items in the display. Response accuracy also decreased on dual- compared to single-target searches: dual-target accuracy was higher than predicted by a model restricting search guidance to a single target-template and lower than predicted by a model simulating two independent single-target searches. These results are consistent with a parallel model of dual-target search in which attentional control is exerted by more than one target-template at a time. The requirement to maintain two target-templates simultaneously, however, appears to impose a reduction in the specificity of the memory representation that guides search for each target.     

Microarrays: how many do you need?

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.     

Retrieval,alignment, and clustering of computational models based on semantic annotations

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.     

Test-retest reliability of the five-repetition sit-to-stand test: a systematic review of the literature involving adults

The 5-repetition sit-to-stand test (FRSTST) is a widely used measure of functional strength, particularly among older adults. The purpose of this review was to summarize the findings of research using the intraclass correlation coefficient (ICC) to describe the test-retest reliability of the FRSTST. A search of 3 electronic databases and hand searches were used to identify relevant articles. Information on the subjects, test sessions and the ICCs reported was abstracted from the articles. The searches identified 10 relevant articles. The ICCs reported in the articles ranged from 0.64 to 0.96. The adjusted mean ICC calculated from the reported ICCs was 0.81. The test-retest reliability of the FRSTST can be interpreted as good to high in most populations and settings.     

A protocol for the update of references to scientific literature in biological databases

Entries in biological databases are usually linked to scientific references. To generate those links and to keep them up-to-date, database maintainers have to continuously scan the scientific literature to select references that are relevant for each single database entry. The continuous growth of both the corpus of scientific literature and the size of biological databases makes this task very hard. We present a protocol intended to assist the updating of an existing set of literature (abstract) links from a single database entry with new references. It consists of taking the set of MEDLINE neighbour references of the existing linked abstracts and evaluating their relevance according to the existing set of abstracts. To test the applicability of the algorithm, we did a simple benchmark of the system using the references associated with the entries of a protein domain database. Human experts found the references that the algorithm scored highly were more relevant to the database entry than those scored lowly, suggesting that the algorithm was useful.     

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher''s experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.     

Is it legitimate to characterize muscle strength using a limited number of measures?

The purpose of this systematic review was to examine the legitimacy of using a single measure or a small set of measures of strength to characterize an individual's overall strength. Briefly, the methods involved: (a) a search of electronic databases, article reference lists, and personal files to identify relevant literature; and (b) a summarizing of that literature. As a result of the searches, 25 relevant articles were identified. The articles reported correlation coefficients, Cronbach's alpha, and factor analysis. Together, these statistics suggest a tendency for different strength measures to be related. A close examination of the relationships, however, suggests that caution should be exercised in characterizing overall strength using a single measure such as grip strength. In conclusion, it may be legitimate to use one or several measures obtained from a single limb to characterize the strength of that limb but not the entire body. What this means practically is that the practitioner interested in characterizing strength of a limb can reduce test burden by testing a limited number of muscle actions of that limb.     

Irreproducibility in searches of scientific literature: A comparative analysis

1. Repeatability is the cornerstone of science, and it is particularly important for systematic reviews. However, little is known on how researchers’ choice of database, and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.
2. We present a comparative analysis of time‐synchronized searches at different institutional locations in the world and evaluate the consistency of hits obtained within each of the search terms using different search platforms.
3. We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science''s Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.
4. To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine‐readable search metadata.

     

Concept-based query expansion for retrieving gene related publications from MEDLINE

Background  

Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.     

PLAN: a web platform for automating high-throughput BLAST searches and for managing and mining results

Background  

BLAST searches are widely used for sequence alignment. The search results are commonly adopted for various functional and comparative genomics tasks such as annotating unknown sequences, investigating gene models and comparing two sequence sets. Advances in sequencing technologies pose challenges for high-throughput analysis of large-scale sequence data. A number of programs and hardware solutions exist for efficient BLAST searching, but there is a lack of generic software solutions for mining and personalized management of the results. Systematically reviewing the results and identifying information of interest remains tedious and time-consuming.     

Impact of PubMed search filters on the retrieval of evidence by physicians

Background:

Physicians face challenges when searching PubMed for research evidence, and they may miss relevant articles while retrieving too many nonrelevant articles. We investigated whether the use of search filters in PubMed improves searching by physicians.

Methods:

We asked a random sample of Canadian nephrologists to answer unique clinical questions derived from 100 systematic reviews of renal therapy. Physicians provided the search terms that they would type into PubMed to locate articles to answer these questions. We entered the physician-provided search terms into PubMed and applied two types of search filters alone or in combination: a methods-based filter designed to identify high-quality studies about treatment (clinical queries “therapy”) and a topic-based filter designed to identify studies with renal content. We evaluated the comprehensiveness (proportion of relevant articles found) and efficiency (ratio of relevant to nonrelevant articles) of the filtered and nonfiltered searches. Primary studies included in the systematic reviews served as the reference standard for relevant articles.

Results:

The average physician-provided search terms retrieved 46% of the relevant articles, while 6% of the retrieved articles were nonrelevant (the ratio of relevant to nonrelevant articles was 1:16). The use of both filters together produced a marked improvement in efficiency, resulting in a ratio of relevant to nonrelevant articles of 1:5 (16 percentage point improvement; 99% confidence interval 9% to 22%; p < 0.003) with no substantive change in comprehensiveness (44% of relevant articles found; p = 0.55).

Interpretation:

The use of PubMed search filters improves the efficiency of physician searches. Improved search performance may enhance the transfer of research into practice and improve patient care.Retrieving health literature is a cornerstone of evidence-based practice. With the rapid increase in available evidence, physicians can no longer rely on one or two key journals to stay current. Increasingly, physicians search bibliographic databases, such as PubMed, for research evidence, which is dispersed across hundreds of journals. Each year, physicians perform over 200 million searches in PubMed.^1,2 Physicians face challenges while searching PubMed and often miss relevant articles while retrieving too many nonrelevant articles.^3–6 Clinical decision-making based on evidence from a search may be impaired if relevant articles are missed. Retrieving many nonrelevant articles impedes the efficiency of searching. Improved search strategies are therefore necessary to retrieve a manageable amount of information. The use of PubMed search filters may help solve this problem. Filters are objectively derived, pretested strategies optimized to help users efficiently retrieve articles for a specific purpose.^7,8PubMed provides two types of clinical search filters: methods-based and topic-based. Methods-based filters (known as clinical queries) were designed to retrieve articles on therapy, diagnosis, prognosis and etiology.^9–13 For example, the clinical queries “therapy” filter is optimized to retrieve publications of randomized controlled trials. Methods-based filters can be used for any clinical discipline and are available for general use in PubMed (www.ncbi.nlm.nih.gov/pubmed/clinical). Topic-based filters, in contrast, are designed to retrieve articles within a specific discipline or topic. For example, the recently developed nephrology filters were optimized to retrieve articles with renal content.¹Physicians can use methods- and topic-based filters alone or in combination. For example, Figure 1A shows a search without search filters for studies about the effectiveness of hepatitis B vaccination in patients with chronic kidney disease. Alternatively, this search could be performed with search filters (Figure 1B). Using filters removes the task of generating and including method-specific or topic-specific terms in a search strategy because the filters act as optimized substitutes. For example, applying the nephrology filter eliminates the need to enter renal terms and synonyms in a search query (e.g., chronic kidney disease, end-stage renal disease, chronic renal failure). The nephrology filter, instead, maximizes the retrieval of all renal content (see the nephrology filter strategy in Figure 1B).Open in a separate windowFigure 1:PubMed searches without (A) and with (B) filters. This figure was created from the PubMed clinical queries Web interface; this page currently does not feature a “clinical category” section. When we performed searches with the nephrology filter (B), we removed the term “chronic kidney disease” because the filter acts as an optimized substitute for clinical content terms.In theory, filters should make searching more efficient; however, empiric evidence of this among physicians is lacking. We conducted this study to determine whether the use of methods-based filters and topic-based filters (alone and in combination) improve the efficiency of physician searches in PubMed. The area of renal medicine is an excellent test model because the literature in this field is dispersed across 400 multidisciplinary journals, and many nephrologists search PubMed for information to guide patient care.^14,15     

Statistical Tests for Associations between Two Directed Acyclic Graphs

Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.     

Text mining and ontologies in biomedicine: making sense of raw text

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.     

Literature mining for the biologist: from information retrieval to biological discovery

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.     

Clustering millions of tandem mass spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.     

Chimpanzees in hepatitis C virus research: 1998–2007

Background  Chimpanzees have been widely used in hepatitis C virus (HCV) research, but their endangered status and high financial and ethical costs have prompted a closer review.
Methods  One hundred and nine articles published in 1998–2007 were analyzed for the number of chimpanzees involved, experimental procedures, objectives and other relevant issues.
Results  The articles described the use of 852 chimpanzees, but accounting for likely multiple uses, the number of individual chimpanzees involved here is estimated to be approximately 500. Most articles addressed immunology and inoculation studies. A significant portion of studies lasted for several months or years. Approximately one half of the individual chimpanzees were each used in 2–10 studies.
Conclusions  Significant financial and scientific resources have been expended in these chimpanzee HCV studies. Discussion addresses troublesome questions presented by some of the reviewed articles, including statistical validity, repeatability, and biological relevance of this model. These concerns merit attention as future approaches to HCV research and research priorities are considered.     

The importance of cultural knowledge and scale for analysing internet search data as a proxy for public interest toward the environment

Recent analyses of Internet search behaviour conclude that the public’s interest in environmental issues is falling (McCallum and Bury, Biodiv Conserv 22:1355–1367, 2013). Ficetola (Biodiv Conserv 22:2983–2988, 2013) argued that the nature of the underpinning data processing may create an artificially declining trend, even when the absolute number of searches increases and public interest is growing. These findings are highly relevant for applied conservation strategies and the public media have quickly picked the message of the alarming fading interest worldwide, the possibility of devastating repercussions and calls for rapid responses in conservation communication. We challenge both analysis by evaluating Internet searches of English and non-English speaking users. The inclusion of information on the linguistic background reveals a much more differentiated picture, with some cultures displaying an increasing interest and others a decreasing interest. These analyses allow a better understanding of the importance of global—local viewpoints, cultural knowledge and cultural differences on the interpretation of underpinning human interest from Internet search patterns. Despite methodological problems limiting the utility of summary data provided by search engines, they can offer powerful information when applied spatially and temporally restricted and analysed alongside suitable benchmark indicators. We discuss that due consideration of methodological caveats is essential to inform the general public about the relevance for conservation without triggering sensationalist or over-generalizing conclusions. Conservation communication needs considering that Internet search engines do not necessarily mirror the interest of many people who are essential for the conservation of biodiversity.     

Knowledge discovery by automated identification and ranking of implicit relationships

MOTIVATION: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set. RESULTS: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.